The increasing demand for analyzing the insights in sports has stimulated a line of productive studies from a variety of perspectives, e.g., health state monitoring, outcome prediction. In this paper, we focus on objectively judging what and where to return strokes, which is still unexplored in turn-based sports. By formulating stroke forecasting as a sequence prediction task, existing works can tackle the problem but fail to model information based on the characteristics of badminton. To address these limitations, we propose a novel Position-aware Fusion of Rally Progress and Player Styles framework (ShuttleNet) that incorporates rally progress and information of the players by two modified encoder-decoder extractors. Moreover, we design a fusion network to integrate rally contexts and contexts of the players by conditioning on information dependency and different positions. Extensive experiments on the badminton dataset demonstrate that ShuttleNet significantly outperforms the state-of-the-art methods and also empirically validates the feasibility of each component in ShuttleNet. On top of that, we provide an analysis scenario for the stroke forecasting problem.
翻译:分析体育洞见的需求不断增长,这从多种角度刺激了一系列富有成效的研究,例如健康状况监测、结果预测。在本文中,我们侧重于客观地判断在转手运动中仍未探索的中风是什么和哪里可以返回。通过将中风预测作为一项序列预测任务,现有作品可以解决这个问题,但不能根据羽毛球的特性来模拟信息。为了解决这些局限性,我们提议建立一个新型的 " REAL进步和玩家风格定位组合框架 " (SuttleNet),其中纳入两个修改过的编码器脱coder提取器的组合进展和参与者信息。此外,我们设计了一个聚合网络,通过调整信息依赖性和不同位置来整合参与者的集合环境和背景。关于羽毛球数据集的广泛实验表明,ShuttleNet大大超越了最先进的方法,并且用经验验证了ShuttleNet中每个组成部分的可行性。此外,我们提供了中风预报问题的分析设想。